大数据笔记（二十二）——大数据实时计算框架Storm

一.

1、对比：离线计算和实时计算
离线计算：MapReduce，批量处理（Sqoop-->HDFS--> MR ---> HDFS）
实时计算：Storm和Spark Sparking，数据实时性（Flume ---> Kafka ---> 流式计算 ---> Redis）

2、常见的实时计算（流式计算）代表
（1）Apache Storm
（2）Spark Streaming
（3）Apache Flink：既可以流式计算，也可以离线计算

二、Storm的体系结构

三、安装和配置Apache Storm
1、前提条件：安装ZooKeeper（Hadoop的HA）

tar -zxvf apache-storm-1.0.3.tar.gz -C ~/training/
设置环境变量：

STORM_HOME=/root/training/apache-storm-1.0.3
export STORM_HOME

PATH=$STORM_HOME/bin:$PATH
export PATH

配置文件: conf/storm.yaml
注意：- 后面有一个空格
: 后面有一个空格

2、Storm的伪分布模式（bigdata11）
18 storm.zookeeper.servers:
19 - "bigdata11"

主节点的信息
23 nimbus.seeds: ["bigdata11"]

每个从节点上的worker个数
25 supervisor.slots:ports:
26 - 6700
27 - 6701
28 - 6702
29 - 6703

任务上传后，保存的目录
storm.local.dir: "/root/training/apache-storm-1.0.3/tmp"

启动Storm：bigdata11
主节点： storm nimbus &
从节点： storm supervisor &
UI: storm ui & ---> http://ip:8080
logviewer:storm logviewer &

3、Storm的全分布模式（bigdata12 bigdata13 bigdata14）
（*）在bigata12上进行配置
storm.zookeeper.servers:
- "bigdata12"
- "bigdata13"
- "bigdata14"

nimbus.seeds: ["bigdata12"]
storm.local.dir: "/root/training/apache-storm-1.0.3/tmp"
supervisor.slots:ports:
- 6700
- 6701
- 6702
- 6703

（*）复制到其他节点
scp -r apache-storm-1.0.3/ root@bigdata13:/root/training
scp -r apache-storm-1.0.3/ root@bigdata14:/root/training

（*）启动
bigdata12: storm nimbus &
storm ui &
storm logviewer &

bigdata13: storm supervisor &
storm logviwer &

bigdata14: storm supervisor &
storm logviwer &

4、Storm的HA（bigdata12 bigdata13 bigdata14）
每台机器都要修改:
nimbus.seeds: ["bigdata12", "bigdata13"]

在bigdata13上，单独启动一个nimbus ----> not leader
还可以单独启动一个UI

四.WordCount数据流动的过程

用Java程序实现：

WordCountSpout.java

package demo;

import java.util.Map;
import java.util.Random;
import java.util.stream.Collector;

import org.apache.jute.Utils;
import org.apache.storm.spout.SpoutOutputCollector;
import org.apache.storm.task.TopologyContext;
import org.apache.storm.topology.OutputFieldsDeclarer;
import org.apache.storm.topology.base.BaseRichSpout;
import org.apache.storm.tuple.Fields;
import org.apache.storm.tuple.Values;

/**
 * @作用：采集数据，送到下一个Bolt组件
 *
 */
public class WordCountSpout extends BaseRichSpout{

    /**
     *
     */
    private static final long serialVersionUID = 1L;

    //定义数据
    private String[] data = {"I love Beijing","I love China","Beijing is the capital of China"};

    private SpoutOutputCollector collector;

    @Override
    public void nextTuple() {
        //每三秒采集一次
        org.apache.storm.utils.Utils.sleep(3000);

        // 由storm框架进行调用，用于接收外部系统产生的数据
        //随机产生一个字符串，代表采集的数据
        int random = new Random().nextInt(3);//3以内随机数

        //采集数据，然后发送给下一个组件
        System.out.println("采集的数据是： "+data[random]);
        this.collector.emit(new Values(data[random]));
    }

    /*
     * SpoutOutputCollector 输出流
     */
    @Override
    public void open(Map arg0, TopologyContext arg1, SpoutOutputCollector collector) {
        // spout组件初始化方法
        this.collector = collector;
    }

    @Override
    public void declareOutputFields(OutputFieldsDeclarer declarer) {
        // 声明输出的schema
        declarer.declare(new Fields("sentence"));
    }

}

WordCountSplitBolt.java

package demo;

import java.util.Map;

import org.apache.storm.task.OutputCollector;
import org.apache.storm.task.TopologyContext;
import org.apache.storm.topology.OutputFieldsDeclarer;
import org.apache.storm.topology.base.BaseRichBolt;
import org.apache.storm.tuple.Fields;
import org.apache.storm.tuple.Tuple;
import org.apache.storm.tuple.Values;

/**
 * 第一个Bolt组件，用于分词操作
 *
 */
public class WordCountSplitBolt extends BaseRichBolt{

    private OutputCollector collector;
    @Override
    public void execute(Tuple tuple) {
        //处理上一个组件发来的数据
        //获取数据
        String line = tuple.getStringByField("sentence");
        //分词
        String[] words = line.split(" ");

        //输出
        for (String word : words) {
            this.collector.emit(new Values(word,1));
        }
    }

    //OutputCollector:bolt组件输出流
    @Override
    public void prepare(Map arg0, TopologyContext arg1, OutputCollector collector) {
        // 对bolt组件初始化
        this.collector = collector;
    }

    @Override
    public void declareOutputFields(OutputFieldsDeclarer declarer) {
        // 声明输出的Schema
        declarer.declare(new Fields("word","count"));
    }

}

WordCountTotalBolt.java

package demo;

import java.util.HashMap;
import java.util.Map;

import org.apache.storm.generated.DistributedRPCInvocations.AsyncProcessor.result;
import org.apache.storm.shade.org.apache.commons.lang.Validate;
import org.apache.storm.task.OutputCollector;
import org.apache.storm.task.TopologyContext;
import org.apache.storm.topology.OutputFieldsDeclarer;
import org.apache.storm.topology.base.BaseRichBolt;
import org.apache.storm.tuple.Fields;
import org.apache.storm.tuple.Tuple;
import org.apache.storm.tuple.Values;

/**
 * 第二个Bolt组件：单词的计数
 *
 */
public class WordCountTotalBolt extends BaseRichBolt{

    private OutputCollector collector;

    private Map<String, Integer> result = new HashMap<>();

    @Override
    public void execute(Tuple tuple) {
        //获取数据：单词、频率：1
        String word = tuple.getStringByField("word");
        int count = tuple.getIntegerByField("count");

        if (result.containsKey(word)) {
            //单词已存在
            int total = result.get(word);
            result.put(word, total+count);
        }else {
            //单词不存在
            result.put(word, count);
        }

        //输出
        System.out.println("输出的结果是： "+ result);
        //发送给下一个组件
        this.collector.emit(new Values(word,result.get(word)));
    }

    @Override
    public void prepare(Map arg0, TopologyContext arg1, OutputCollector collector) {
        // TODO Auto-generated method stub
        this.collector = collector;
    }

    @Override
    public void declareOutputFields(OutputFieldsDeclarer declarer) {
        // TODO Auto-generated method stub
        declarer.declare(new Fields("word","total"));
    }

}

WordCountTopology.java

package demo;

import org.apache.storm.Config;
import org.apache.storm.LocalCluster;
import org.apache.storm.StormSubmitter;
import org.apache.storm.generated.AlreadyAliveException;
import org.apache.storm.generated.AuthorizationException;
import org.apache.storm.generated.InvalidTopologyException;
import org.apache.storm.generated.StormTopology;
import org.apache.storm.hdfs.bolt.HdfsBolt;
import org.apache.storm.hdfs.bolt.format.DefaultFileNameFormat;
import org.apache.storm.hdfs.bolt.format.DelimitedRecordFormat;
import org.apache.storm.hdfs.bolt.rotation.FileSizeRotationPolicy;
import org.apache.storm.hdfs.bolt.rotation.FileSizeRotationPolicy.Units;
import org.apache.storm.hdfs.bolt.sync.CountSyncPolicy;
import org.apache.storm.redis.bolt.RedisStoreBolt;
import org.apache.storm.redis.common.config.JedisPoolConfig;
import org.apache.storm.redis.common.mapper.RedisDataTypeDescription;
import org.apache.storm.redis.common.mapper.RedisStoreMapper;
import org.apache.storm.topology.IRichBolt;
import org.apache.storm.topology.TopologyBuilder;
import org.apache.storm.tuple.Fields;
import org.apache.storm.tuple.ITuple;

public class WordCountTopology {

    public static void main(String[] args) throws Exception {
        //设置用户为root权限
        System.setProperty("HADOOP_USER_NAME", "root");
        //创建一个任务：Topology = spout + bolt(s)

        TopologyBuilder builder = new TopologyBuilder();

        //设置任务的第一个组件：spout组件
        builder.setSpout("mywordcount_spout", new WordCountSpout());
        //builder.setSpout("mywordcount_spout", createKafkaSpout());

        //设置任务的第二个组件：bolt组件，拆分单词
        builder.setBolt("mywordcount_split", new WordCountSplitBolt()).shuffleGrouping("mywordcount_spout");

        //设置任务的第三个组件：bolt组件，计数
        builder.setBolt("mywordcount_total", new WordCountTotalBolt()).fieldsGrouping("mywordcount_split", new Fields("word"));

        //设置任务的第四个bolt组件，将结果写入Redis
        //builder.setBolt("mywordcount_redis", createRedisBolt()).shuffleGrouping("mywordcount_total");

        //设置任务的第四个bolt组件，将结果写入HDFS
        //builder.setBolt("mywordcount_hdfs", createHDFSBolt()).shuffleGrouping("mywordcount_total");

        //设置任务的第四个bolt组件，将结果写入HBase
        //builder.setBolt("mywordcount_hdfs", new WordCountHBaseBolt()).shuffleGrouping("mywordcount_total");        

        //创建任务
        StormTopology topology = builder.createTopology();

        //配置参数
        Config conf = new Config();

        //提交任务
        //方式1：本地模式（直接在eclipse运行）
        LocalCluster cluster = new LocalCluster();
        cluster.submitTopology("mywordcount", conf, topology);

        // 方式2 集群模式: storm jar temp/storm.jar demo.WordCountTopology MyStormWordCount
        //StormSubmitter.submitTopology(args[0], conf, topology);
    }

    private static IRichBolt createHDFSBolt() {
        // 创建一个HDFS的Bolt组件，写入到HDFS
        HdfsBolt bolt = new HdfsBolt();

        //指定HDFS位置：namenode地址
        bolt.withFsUrl("hdfs://192.168.153.11:9000");

        //数据保存在HDFS哪个目录
        bolt.withFileNameFormat(new DefaultFileNameFormat().withPath("/stormresult"));

        //ָ指定key和value的分隔符：Beijing|10
        bolt.withRecordFormat(new DelimitedRecordFormat().withFieldDelimiter("|"));

        //生成文件的策略：每5M生成一个文件
        bolt.withRotationPolicy(new FileSizeRotationPolicy(5.0f,Units.MB));

        //与HDFS进行数据同步的策略:tuple数据达到1K同步一次
        bolt.withSyncPolicy(new CountSyncPolicy(1024));

        return bolt;
    }

    private static IRichBolt createRedisBolt() {
        // 创建一个Redis的bolt组件，将数据写入redis中
        //创建一个Redis的连接池

        JedisPoolConfig.Builder builder = new JedisPoolConfig.Builder();
        builder.setHost("192.168.153.11");
        builder.setPort(6379);
        JedisPoolConfig poolConfig = builder.build();

        //storeMapper: 存入Redis中数据的格式
        return new RedisStoreBolt(poolConfig, new RedisStoreMapper() {

            @Override
            public RedisDataTypeDescription getDataTypeDescription() {
                // 声明存入Redis的数据类型
                return new RedisDataTypeDescription(RedisDataTypeDescription.RedisDataType.HASH,"wordcount");
            }

            @Override
            public String getValueFromTuple(ITuple tuple) {
                // 从上一个组件接收的value
                return String.valueOf(tuple.getIntegerByField("total"));
            }

            @Override
            public String getKeyFromTuple(ITuple tuple) {
                // 从上一个组件接收的key
                return tuple.getStringByField("word");
            }
        });
    }

}

集成redis结果：

集成hdfs:

集成hbase:

WordCountHBaseBolt.java

package demo;

import java.util.Map;

import org.apache.hadoop.hbase.client.HTable;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.generated.master.table_jsp;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.storm.task.OutputCollector;
import org.apache.storm.task.TopologyContext;
import org.apache.storm.topology.IRichBolt;
import org.apache.storm.topology.OutputFieldsDeclarer;
import org.apache.storm.topology.base.BaseRichBolt;
import org.apache.storm.tuple.Tuple;

/**
 * 创建一个HBASE的表：create 'result','info'
 *
 */
public class WordCountHBaseBolt extends BaseRichBolt {

    //定义一个Hbase的客户端
    private HTable htable;

    @Override
    public void execute(Tuple tuple) {
        //得到上一个组件处理的数据
        String word = tuple.getStringByField("word");
        int total = tuple.getIntegerByField("total");

        //创建一个put对象
        Put put = new Put(Bytes.toBytes(word));
        //列族：info 列：word 值：word
        put.add(Bytes.toBytes("info"), Bytes.toBytes("word"), Bytes.toBytes(word));
        put.add(Bytes.toBytes("info"), Bytes.toBytes("total"), Bytes.toBytes(String.valueOf(total)));
        try {
            htable.put(put);
        } catch (Exception e) {
            e.printStackTrace();
        }
    }

    @Override
    public void prepare(Map arg0, TopologyContext arg1, OutputCollector arg2) {
        // 初始化：指定HBASE的相关信息

    }

    @Override
    public void declareOutputFields(OutputFieldsDeclarer arg0) {
        // TODO Auto-generated method stub

    }

}

通过hbase shell打开hbase命令行

五.Strom任务提交的过程

1.客户端提交任务

2.创建任务的本地目录

3.nimbus分配任务到zookeeper

4.supervisor从ZK获取分配的任务，启动对应的worker来执行任务

5.将任务执行的心跳存入ZK

6.nimbus监听任务的执行

六、Storm内部通信的机制

任务的执行：worker中的Executor